A curated list of papers & ressources linked to open set recognition, out-of-distribution, open set domain adaptation, and open world recognition

Note that:

• This list is not exhaustive.
• Tables use alphabetical order for fairness.

• Tutorials

• Papers

  • Open Set Recognition
    • Traditional Machine Learning Methods-based
    • Deep Neural Network-based
    • Adversarial Learning-based
    • EVT-based
  • Out-of-Distribution
  • Anomaly Detection
  • Open Set Domain Adaptation
  • Open World Recognition

• License

• Contributing

• Toward Open Set Recognition, Scheirer W J, de Rezende Rocha A, Sapkota A, et al. (PAMI, 2013).

• Towards Open World Recognition, Bendale A, Boult T. (CVPR, 2015).

• Lifelong Machine Learning, Zhiyuan Chen and Bing Liu. (2018).

• Recent Advances in Open Set Recognition: A Survey, Geng C, Huang S, Chen S. (arXiv, 2018).

• Recent Advances in Open Set Recognition: A Survey v2, Chuanxing Geng, Sheng-jun Huang, Songcan Chen. (arXiv, 2019).

• Toward Open Set Recognition, Scheirer W J, de Rezende Rocha A, Sapkota A, et al. (PAMI, 2013).[code].

• Probability models for open set recognition, Scheirer W J, Jain L P, Boult T E. (PAMI, 2014). [code].
• Multi-class open set recognition using probability of inclusion, Jain L P, Scheirer W J, Boult T E. (ECCV, 2014). [code].

• Breaking the closed world assumption in text classification, Fei G, Liu B. (NAACL, 2016).

• Sparse representation-based open set recognition, Zhang H, Patel V M. (PAMI, 2017).
• Best fitting hyperplanes for classification, Cevikalp H. (PAMI, 2017). [code].
• Polyhedral conic classifiers for visual object detection and classification, Cevikalp H, Triggs B. Rigling B D. (CVPR, 2017).
• Fast and Accurate Face Recognition with Image Sets, Cevikalp H, Yavuz H S. (ICCVW, 2017). [code]
• Nearest neighbors distance ratio open-set classifier, Júnior P R M, de Souza R M, Werneck R O, et al. (Machine Learning, 2017).

• Data-Fusion Techniques for Open-Set Recognition Problems, Neira M A C, Júnior P R M, Rocha A, et al. (IEEE Access, 2018).
• Towards open-set face recognition using hashing functions, Vareto R, Silva S, Costa F, et al. (IJCB, 2018). [code].
• Learning to Separate Domains in Generalized Zero-Shot and Open Set Learning: a probabilistic perspective, Hanze Dong, Yanwei Fu, Leonid Sigal, Sung Ju Hwang, Yu-Gang Jiang, Xiangyang Xue. (arXiv, 2018).

• Specialized Support Vector Machines for Open-set Recognition, Pedro Ribeiro Mendes Júnior, Terrance E. Boult, Jacques Wainer, Anderson Rocha (arXiv, 2019).

• A bounded neural network for open set recognition, Cardoso D O, França F, Gama J. (IJCNN, 2015).

• Towards open set deep networks, Bendale A, Boult T E. (CVPR, 2016). [code].

• Weightless neural networks for open set recognition, Cardoso D O, Gama J, França F M G. (Machine Learning, 2017).
• Adversarial Robustness: Softmax versus Openmax, Rozsa A, Günther M, Boult T E. (arXiv, 2017).
• DOC: Deep open classification of text documents, Shu L, Xu H, Liu B. Doc. (arXiv, 2017). [code].

• Open category detection with PAC guarantees, Si Liu, Risheek Garrepalli, Thomas G. Dietterich, Alan Fern, Dan Hendrycks. (ICML, 2018). [code].
• Open Set Text Classification using Convolutional Neural Networks, Prakhya S, Venkataram V, Kalita J. (NLPIR, 2018).
• Learning a Neural-network-based Representation for Open Set Recognition, Hassen M, Chan P K. (arXiv, 2018).
• Unseen Class Discovery in Open-world Classification, Shu L, Xu H, Liu B. (arXiv, 2018).
• Reducing Network Agnostophobia, Akshay Raj Dhamija, Manuel Günther, Terrance E. Boult. (NeurIPS 2018). [code].

• The Importance of Metric Learning for Robotic Vision: Open Set Recognition and Active Learning, Benjamin J. Meyer, Tom Drummond. (ICRA, 2019).
• Deep CNN-based Multi-task Learning for Open-Set Recognition, Poojan Oza, Vishal M. Patel. (arXiv, 2019, Under Review).
• Classification-Reconstruction Learning for Open-Set Recognition, Ryota Yoshihashi, Wen Shao, Rei Kawakami, Shaodi You, Makoto Iida, Takeshi Naemura. (CVPR, 2019).
• Alignment Based Matching Networks for One-Shot Classification and Open-Set Recognition, Paresh Malalur, Tommi Jaakkola. (arXiv, 2019).
• Open-Set Recognition Using Intra-Class Splitting, Patrick Schlachter, Yiwen Liao, Bin Yang. (EUSIPCO, 2019).
• Experiments on Open-Set Speaker Identification with Discriminatively Trained Neural Networks, Stefano Imoscopi, Volodya Grancharov, Sigurdur Sverrisson, Erlendur Karlsson, Harald Pobloth. (arXiv, 2019).
• Large-Scale Long-Tailed Recognition in an Open World, ZiweiLiu, ZhongqiMiao, XiaohangZhan, et al. (CVPR, Oral, 2019).[code]
• Open Set Recognition Through Deep Neural Network Uncertainty: Does Out-of-Distribution Detection Require Generative Classifiers?, Martin Mundt, Iuliia Pliushch, Sagnik Majumder, Visvanathan Ramesh. (ICCVW, 2019). [code]
• Deep Transfer Learning for Multiple Class Novelty Detection, Pramuditha Perera, Vishal M. Patel. (CVPR, 2019). [code]
• From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer, Haipeng Xiong, Hao Lu, Chengxin Liu, Liang Liu, Zhiguo Cao, Chunhua Shen. (ICCV, 2019). [code]

• Generative openmax for multi-class open set classification, Ge Z Y, Demyanov S, Chen Z, et al. (arXiv, 2017).
• Open-category classification by adversarial sample generation, Yu Y, Qu W Y, Li N, et al. (IJCAI, 2017). [code]

• Open Set Adversarial Examples, Zhedong Z, Liang Z, Zhilan H, et al. (arXiv, 2018).
• Open Set Learning with Counterfactual Images, Neal L, Olson M, Fern X, et al. (ECCV, 2018). [code]

• Open-set human activity recognition based on micro-Doppler signatures, Yang Y, Hou C, Lang Y, et al. (Pattern Recognition, 2019).
• C2AE: Class Conditioned Auto-Encoder for Open-set Recognition, Poojan Oza, Vishal M Patel. (CVPR, 2019, oral).

• The extreme value machine, Rudd E M, Jain L P, Scheirer W J, et al. (PAMI, 2018). [code]
• Extreme Value Theory for Open Set Classification-GPD and GEV Classifiers, Vignotto E, Engelke S. (arXiv, 2018).

• Collective decision for open set recognition, Chuanxing Geng, Songcan Chen. (arXiv, 2019).

• A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks. Dan Hendrycks and Kevin Gimpel. (ICLR, 2017). [code].

• Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks. Shiyu Liang, Yixuan Li, R. Srikant. (ICLR, 2018). [code].
• Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. Kimin Lee, Honglak Lee, Kibok Lee, Jinwoo Shin. (ICLR, 2018). [code]
• WAIC, but Why? Generative Ensembles for Robust Anomaly Detection. Hyunsun Choi, Eric Jang, Alexander A. Alemi. (ArXiv, 2018).
• A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. Kimin Lee, Kibok Lee, Honglak Lee, Jinwoo Shin. (NeurIPS, 2018). [code]

• Deep Anomaly Detection with Outlier Exposure, Dan Hendrycks, Mantas Mazeika, Thomas Dietterich. (ICLR, 2019). [code]
• Do Deep Generative Models Know What They Don't Know?. Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, Balaji Lakshminarayanan. (ICLR, 2019).
• Likelihood Ratios for Out-of-Distribution Detection. Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A. DePristo, Joshua V. Dillon, Balaji Lakshminarayanan. (NeurIPS, 2019). [code]
• Unsupervised Out-of-Distribution Detection by Maximum Classifier Discrepancy. Qing Yu, Kiyoharu Aizawa. (ICCV, 2019)
• Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem. Matthias Hein, Maksym Andriushchenko, Julian Bitterwolf. (CVPR 2019). [code]
• Outlier Exposure with Confidence Control for Out-of-Distribution Detection. Aristotelis-Angelos Papadopoulos, Mohammad Reza Rajati, Nazim Shaikh, Jiamian Wang. (ArXiv, 2019). [code]

• Input Complexity and Out-of-distribution Detection with Likelihood-based Generative Models. Joan Serrà, David Álvarez, Vicenç Gómez, Olga Slizovskaia, José F. Núñez, Jordi Luque. (ICLR, 2020)

need to survey more..

• Deep Anomaly Detection Using Geometric Transformations. Izhak Golan, Ran El-Yaniv. (NeurIPS, 2018). [code].

• Classification-Based Anomaly Detection for General Data. Liron Bergman, Yedid Hoshen. (ICLR, 2020).

• Open Set Domain Adaptation, Pau Panareda Busto, Juergen Gall. (ICCV 2017).
• Label Efficient Learning of Transferable Representations across Domains and Tasks, Zelun Luo, Yuliang Zou, Judy Hoffman, Li Fei-Fei. (NeurIPS 2017).

• Open set domain adaptation by backpropagation, Kuniaki Saito, Shohei Yamamoto, Yoshitaka Ushiku, Tatsuya Harada. (ECCV 2018).

• Separate to Adapt: Open Set Domain Adaptation via Progressive Separation. Hong Liu, Zhangjie Cao, Mingsheng Long, Jianmin Wang, Qiang Yang. (CVPR 2019).
• Unsupervised Open Domain Recognition by Semantic Discrepancy Minimization. Junbao Zhuo, Shuhui Wang, Shuhao Cui, Qingming Huang. (CVPR 2019).
• Weakly Supervised Open-Set Domain Adaptation by Dual-Domain Collaboration. Shuhan Tan, Jiening Jiao, Wei-Shi Zheng. (CVPR 2019). [code]
• Learning Factorized Representations for Open-set Domain Adaptation, Mahsa Baktashmotlagh, Masoud Faraki, Tom Drummond, Mathieu Salzmann. (ICLR 2019).
• Known-class Aware Self-ensemble for Open Set Domain Adaptation, Qing Lian, Wen Li, Lin Chen, Lixin Duan. (arXiv 2019).
• Open Set Domain Adaptation: Theoretical Bound and Algorithm, Zhen Fang, Jie Lu, Feng Liu, Junyu Xuan, Guangquan Zhang. (arXiv 2019).
• Open Set Domain Adaptation for Image and Action Recognition, Pau Panareda Busto, Ahsan Iqbal, Juergen Gall. (arXiv 2019).
• Attract or Distract: Exploit the Margin of Open Set, Qianyu Feng, Guoliang Kang, Hehe Fan, Yi Yang. (ICCV 2019).

• Towards Open World Recognition, Bendale A, Boult T. (CVPR, 2015).

• Learning Cumulatively to Become More Knowledgeable, Geli Fei, Shuai Wang, Bing Liu. (KDD, 2016).
• Online open world recognition, De Rosa R, Mensink T, Caputo B. (arXiv, 2016).

• Open-World Visual Recognition Using Knowledge Graphs, Lonij V, Rawat A, Nicolae M I. (arXiv, 2017).

• Unseen Class Discovery in Open-world Classification, Shu L, Xu H, Liu B. (arXiv, 2018).
• The extreme value machine, Rudd E M, Jain L P, Scheirer W J, et al. (PAMI, 2018).
• Learning to Accept New Classes without Training, Xu H, Liu B, Shu L, et al. (arXiv, 2018).
• ODN: Opening the Deep Network for Open-Set Action Recognition, Shi Y, Wang Y, Zou Y, et al. (ICME, 2018).

• P-ODN: Prototype based Open Deep Network for Open Set Recognition, Yu Shu, Yemin Shi, Yaowei Wang, Tiejun Huang, Yonghong Tian. (arXiv 2019).
• Learning and the Unknown: Surveying Steps Toward Open World Recognition, Terrance Boult, Steve Cruz, Akshay Dhamija, Manuel Günther, James Henrydoss, Walter J. Scheirer. (AAAI, 2019).
• Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition. Martin Mundt, Sagnik Majumder, Iuliia Pliushch, Visvanathan Ramesh. (arXiv 2019).
• Open-world Learning and Application to Product Classification. Hu Xu, Bing Liu, Lei Shu, P. Yu. (WWW 2019). [code]

License

To the extent possible under law, Guangyao Chen has waived all and related or neighboring rights to this work.

Please see CONTRIBUTING for details.

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